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The hype around AI is pervasive, and organizations across industries are investing in what has quickly become a must-have technology. As fast as this new AI arms race has heated up, many organizations are left wondering if they are already late, and that fear of missing out (FOMO) can quickly turn into a panic that they are behind the competition and destined to be on the outside looking in.

Spoiler alert: they are not.

However, its not because this is a case of unwarranted hypeadoption of AI technology is growing rapidlybut the sense of get on board with everything now because the last train is leaving the station is overplayed. Furthermore, FOMO is never a good reason to do somethingespecially deploying a cutting-edge technology that is still evolving with new use cases being thought up and brought to market at light speed. Per a recent study, 42% of CIOs do not expect to see positive ROI in their AI investments for at least 2-3 years. The significant time and resource investment in AI is a marathonnot a sprint. So, then, what is an organization to do? Maintain focus, find your center, and execute. Here is how.

First, we must acknowledge that we have seen a version of this movie before. The market gets a disruptive new technology (PC, Dotcom, Smartphone, Big Data, SaaS, Cloud, etc.) that promises to fundamentally shift the way business is done, and organizations immediately react as though everything must go to it right away. Let us look at one of these examples. With the cloud, use cases were wide open, and organizations jumped in headlong, ceding massive amounts of data, control, and even large budgets to a handful of companies with no plan B.

Only later, though, did they realize that these benefits came with some tangible challenges: vendor lock impacting the promise of cheap scalability; concerns about visibility, accessibility, and security of data; ability to integrate with other areas of their organizations; among others. While the wonderful promise of cloud has come to fruition, the industry has also learned that there is no one-size-fits-all approach, and organizations must be deliberate in their strategies.

The key lesson learned from the disruption brought on by cloud technologies was the importance of maintaining focus on the desired outcome before blindly allocating IT resources. Today, the same holds true with AI. As its usage becomes increasingly pervasive, organizations must strike the right balance between exploring its wide-open adoption and concentrating investment on clearly defined projects that will produce tangible and measurable results.

There is a sense of urgency in all organizations wanting to deploy AI, but they must also remain grounded in best practices and lessons learned. In short, yes. They need to get on board, but they need to be realistic and deploy toward a tangible result where ROI is noticeable and impactful not just a sweeping promise.

One of the more recent challenges with determining the correct course of action with AI is dierentiating among the variety of AI/ML technologies. For example, while most organizations understand the main dierences between traditional AI andGenerative AI, they also exist in a host of dierent flavors and come with a constant stream of new concepts and models. The technology itself can be completely overwhelming, and per the recent CIO survey, less than half (49%) believe their IT departments have AI ready technical skills. Confidence in AI readiness wanes even further when considering other areas/functions within the organization such as data and analytics reporting or security infrastructure.

Yet, flashy and exciting as it is, the technology is only part of itthe what. Organizations need to maintain their focus on the why. It is important to take time to understand the models available, but it is critical to tie them to use cases and outcomes when making decisions about which make sense to implement. Organizations should know their own pain points and areas for improvement, and they can always partner with industry leaders who can help them determine what AI technology to use and where AI can have an immediate positive impact.

Organizations should get started in an area where they will see resultswhatever those may be (new revenue streams, call center productivity, impact on employee experience, revamps of painful processes, etc.). Furthermore, the results do not immediately need to have a massive impact on the bottom line to count. They simply need to be positive, tangible, and measurable. Success builds on itself. From there, organizations can build on that early momentum to find more and more use cases that would benefit from AI, but they must still maintain their focus on their own desired outcomes. This requires blocking out the noise and market FUD about competitors or what some aspirational companies are doing with AI.

Market FUD is especially dangerous when organizations are comparing themselves to their industry peers, as it can be easy to look at competitors and see early examples of their AI successes. However, in many cases, these ideas or concepts are the ones they want to share, and organizations are far from the promise of scaling or even implementing AI across their businesses.

With the unrelenting hype around AI, it is easy to see how organizations can feel that they need to go all in on the technology right away for fear of being left behind. The reality, though, is that organizations are not launching AI initiatives at any kind of scalethat will take time.

The smartest ones are not standing still and are finding the specific areas in which they can apply AI to drive a tangible positive result and build on these early successes. Furthermore, they are not navigating this solo. The endless stream of innovations and models are a good reminder to seek the expertise that can help make an AI investment go smoothly. As with every disruptive technology, aligning with the right partner who understands AI from all angles can help make informed decisions on how to go about getting started. From an initial project showing positive results, organizations can set the stage for additional use cases and begin to scale their AI eorts.

[To share your insights with us, please write topsen@itechseries.com]

Read more:
Don't Panic: Why AI FOMO is Overblown - AiThority

The foundation honoring the late creator of Star Trek has announced a $1 million prize for startups using AI to shape a brighter future.

Gene Roddenberry's vision for humanity as seen in Star Trek was one where humanity coexisted peacefully, putting aside material wants in pursuit of scientific curiosity and bettering oneself.

This years Roddenberry Prize contest focuses on AI and machine learning in the hopes of unearthing solutions that align with the vision of the shows creator.

Judges are looking for AI and machine learning technologies with real-world impact capable of scaling to support billions of people.

Proposed solutions must be respectful of individual rights, be designed to avoid biases and support at least one of the United Nations 17 Sustainable Development Goals.

The foundation, established by Roddenberrys family following his death in 1991, said the competition affirms Roddenberrys confidence in humanitys wisdom and creativity to build a better future.

As AI becomes more powerful and ubiquitous, we call for its use in service of a more equitable and prosperous world in which all of us, regardless of our background, can thrive, according to the foundation.

The contest will feature three rounds: an exploratory first round, followed by a deeper dive with a select group in the second round. The final round will include hour-long meetings with five startups in October and November.

Related:Future AI Could Share Knowledge Like the Borg in Star Trek

The application deadline is July 12.

The competition is open to global startups that have raised seed funding and not exceeded series A. Nonprofit entries must have an annual budget of less than $5 million.

As we enter the AI era, we find ourselves at a critical juncture in human history, poised on the brink of profound technological transformation, according to the foundation. The rapid advancement of AI promises to revolutionize virtually every aspect of society, from the way we work and communicate to how we navigate the complexities of the modern world.

AI was greatly interwoven in the lore of Roddenberrys Star Trek universe, from the interactive computer systems found on Starships to the Emergency Medical Holograms capable of treating patients on Voyager.

One AI-related story still fascinates scholars to this day: The Next Generation episode The Measure of a Man, in which a legal hearing is called to determine whether android crewmember Data was sentient or merely a machine.

Captain Kirk himself was turned into an AI chatbot back in 2021. Actor William Shatner was immortalized by StoryFile, a company he part-owns. However, the StoryFile filed for Chapter 11 bankruptcy protection earlier this year.

Related:NASA Scientist Evokes Star Trek Diversity to Enable Interplanetary Travel

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Star Trek Creator's Foundation Offers $1M Prize for AI Promoting Good - AI Business

Video Annotation Service for Machine Learning Market

Moreover, The report provides a professional in-depth examination of the Video Annotation Service for Machine Learning Market's current scenario, CAGR, gross margin, revenue, price, production growth rate, volume, value, market share, and growth are among the market data assessed and re-validation in the research. The report will also cover key agreements, collaborations, and global partnerships soon to change the dynamics of the market on a global scale. Detailed company profiling enables users to evaluate company shares analysis, emerging product lines, the scope of New product development in new markets, pricing strategies, innovation possibilities, and much more.

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The purpose of this market analysis is to estimate the size and growth potential of the market based on the kind of product, the application, the industry analysis, and the area. Also included is a comprehensive competitive analysis of the major competitors in the market, including their company profiles, critical insights about their product and business offerings, recent developments, and important market strategies.

The Leading Players involved in the global Video Annotation Service for Machine Learning market are:

iMerit HabileData Keymakr Sama Mindy Support DamcoGroup Anolytics.ai TaskUs AI Wakforce Infosearch BPO Infosys Cogito DIGI-TEXX Smartone Maxicus SunTec.AI Kotwel GTS Pixta AI

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According to the report, the Video Annotation Service for Machine Learning Market is segmented in the following ways which fulfill the market data needs of multiple stakeholders across the industry value chain -

Segmentation by Type:

2D Video Annotation Service 3D Video Annotation Service

Segmentation by Applications:

Autonomous Vehicles Healthcare and Medical Imaging Retail Sports and Entertainment Agriculture Manufacturing and Industrial Automation Others

Trends and Opportunities of the Global Video Annotation Service for Machine Learning Market:

The global Video Annotation Service for Machine Learning market has seen several trends in recent years, and understanding these trends is crucial to stay ahead of the competition. The global Video Annotation Service for Machine Learning market also presents several opportunities for players in the market. The increasing demand for Video Annotation Service for Machine Learning in various industries presents several growth opportunities for players in the market.

Regional Outlook:

The following section of the report offers valuable insights into different regions and the key players operating within each of them. To assess the growth of a specific region or country, economic, social, environmental, technological, and political factors have been carefully considered. The section also provides readers with revenue and sales data for each region and country, gathered through comprehensive research. This information is intended to assist readers in determining the potential value of an investment in a particular region.

North America: USA, Canada, Mexico, etc. Asia-Pacific: China, Japan, Korea, India, and Southeast Asia The Middle East and Africa: Saudi Arabia, the UAE, Egypt, Turkey, Nigeria, and South Africa Europe: Germany, France, the UK, Russia, and Italy South America: Brazil, Argentina, Columbia, etc.

Research Methodology:

Research Objectives: This section provides an overview of the research study's primary objectives, encompassing the research questions and hypotheses that will be addressed. Research Design: The following section presents the comprehensive outline of the research design, encompassing the selected approach for the study (quantitative, qualitative, or mixed-methods), the methodologies utilized for data collection (surveys, interviews, focus groups), and the sampling strategy employed (random sampling, stratified sampling). Data Collection: This section involves gathering information from primary and secondary sources. Primary sources included the use of survey questionnaires and interview guides, while secondary sources encompassed existing data from reputable publications and databases. Data collection procedures involved meticulous steps such as data cleaning, coding, and entry to ensure the accuracy and reliability of the collected data Data Analysis: The data were analyzed using various methods including statistical tests, qualitative coding, and content analysis. Limitations: The study's limitations encompass potential biases, errors in data sources, and overall data constraints.

Highlights of the Report:

For the period 2024-2031, accurate market size and compound annual growth rate (CAGR) predictions are provided. Exploration and in-depth evaluation of growth potential in major segments and geographical areas. Company profiles of the top players in the global Video Annotation Service for Machine Learning Market are provided in detail. Comprehensive investigation of innovation and other market developments in the global Video Annotation Service for Machine Learning Market. Industry value chain and supply chain analysis that is dependable. A thorough examination of the most significant growth drivers, limitations, obstacles, and future prospects is provided.

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Author Bio:

Money Singh is a seasoned content writer with over four years of experience in the market research sector. Her expertise spans various industries, including food and beverages, biotechnology, chemical and materials, defense and aerospace, consumer goods, etc. (https://www.linkedin.com/in/money-singh-590844163)

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Worldwide Market Reports is your one-stop repository of detailed and in-depth market research reports compiled by an extensive list of publishers from across the globe. We offer reports across virtually all domains and an exhaustive list of sub-domains under the sun. The in-depth market analysis by some of the most vastly experienced analysts provides our diverse range of clients from across all industries with vital decision-making insights to plan and align their market strategies in line with current market trends.

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Upcoming Opportunities in Video Annotation Service for Machine Learning Market: Future Trend and Analysis of Key ... - openPR

DeepChecks is a Python package that provides a wide variety of built-in checks to test for issues with model performance, data distribution, data integrity, and more.

In this tutorial, we will learn about DeepChecks and use it to validate the dataset and test the trained machine learning model to generate a comprehensive report. We will also learn to test models on specific tests instead of generating full reports.

Machine learning testing is essential for ensuring the reliability, fairness, and security of AI models. It helps verify model performance, detect biases, enhance security against adversarial attacks especially in Large Language Models (LLMs), ensure regulatory compliance, and enable continuous improvement. Tools like Deepchecks provide a comprehensive testing solution that addresses all aspects of AI and ML validation from research to production, making them invaluable for developing robust, trustworthy AI systems.

In this getting started guide, we will load the dataset and perform a data integrity test. This critical step ensures that our dataset is reliable and accurate, paving the way for successful model training.

It will take a few second to generate the report.

The data integrity report contains test results on:

Lets train our model and then run a model evaluation suite to learn more about model performance.

The model evaluation report contains the test results on:

There are other tests available in the suite that didn't run due to the ensemble type of model. If you ran a simple model like logistic regression, you might have gotten a full report.

If you don't want to run the entire suite of model evaluation tests, you can also test your model on a single check.

For example, you can check label drift by providing the training and testing dataset.

As a result, you will get a distribution plot and drift score.

You can even extract the value and methodology of the drift score.

The next step in your learning journey is to automate the machine learning testing process and track performance. You can do that with GitHub Actions by following the Deepchecks In CI/CD guide.

In this beginner-friendly, we have learned to generate data validation and machine learning evaluation reports using DeepChecks. If you are having trouble running the code, I suggest you have a look at the Machine Learning Testing With DeepChecks Kaggle Notebook and run it yourself.

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in technology management and a bachelor's degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.

Read more here:
Beginners Guide to Machine Learning Testing With DeepChecks - KDnuggets